Online Palmprint Identification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Competitive Coding Scheme for Palmprint Verification
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Robust Direction Estimation of Gradient Vector Field for Iris Recognition
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Ordinal Palmprint Represention for Personal Identification
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Graph matching iris image blocks with local binary pattern
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
Accurate Palmprint Recognition Using Spatial Bags of Local Layered Descriptors
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Palmprint Recognition Based on Regional Rank Correlation of Directional Features
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Palmprint authentication using a symbolic representation of images
Image and Vision Computing
Palmprint recognition using coarse-to-fine statistical image representation
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
A Comparative Study of Palmprint Recognition Algorithms
ACM Computing Surveys (CSUR)
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Palmprint recognition, as a reliable personal identity check method, has been receiving increasing attention during recent years. According to previous work, local texture analysis supplies the most promising framework for palmprint image representation. In this paper, we propose a novel palmprint recognition method by combining statistical texture descriptions of local image regions and their spatial relations. In our method, for each image block, a spatial enhanced histogram of gradient directions is used to represent discriminative texture features. Furthermore, we measure similarity between two palmprint images using a simple graph matching scheme, making use of structural information. Experimental results on two large palmprint databases demonstrate the effectiveness of the proposed approach.